import random
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

%matplotlib inline

| Import Data

df = pd.read_csv("../data/raw/Employee-Attrition.csv")
df.head()

	Age	Attrition	BusinessTravel	DailyRate	Department	DistanceFromHome	Education	EducationField	EmployeeCount	EmployeeNumber	...	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	41	Yes	Travel_Rarely	1102	Sales	1	2	Life Sciences	1	1	...	1	80	0	8	0	1	6	4	0	5
1	49	No	Travel_Frequently	279	Research & Development	8	1	Life Sciences	1	2	...	4	80	1	10	3	3	10	7	1	7
2	37	Yes	Travel_Rarely	1373	Research & Development	2	2	Other	1	4	...	2	80	0	7	3	3	0	0	0	0
3	33	No	Travel_Frequently	1392	Research & Development	3	4	Life Sciences	1	5	...	3	80	0	8	3	3	8	7	3	0
4	27	No	Travel_Rarely	591	Research & Development	2	1	Medical	1	7	...	4	80	1	6	3	3	2	2	2	2

5 rows × 35 columns

df.shape

(1470, 35)

| Data Wrangling

def generate_meta_signals(df):
    meta_df = df.isna().sum().reset_index()
    meta_df['% of Total Values'] = meta_df[0]/len(df)
    meta_df = pd.concat([meta_df, pd.DataFrame(df.dtypes, columns=['dtypes']).reset_index(drop=True)], axis=1)
    d = df.describe().T.reset_index()
    meta_df = meta_df.merge(d, on=['index'], how='left')
    vcs = [pd.DataFrame(df[x].value_counts(normalize=True)).T for x in 
    list(df.columns)]
    vcs= [pd.DataFrame((x.idxmax(axis=1), x.max(axis=1))).T.reset_index() for x in vcs if len(list(x.columns)) > 0]
    meta_df = meta_df.merge(pd.concat(vcs), on=['index'], how='left') 
    meta_df.columns = ['Index', 'Missing Values', '% of Total Values', 'dtypes', 'count', 'mean', 'std', 'min', '25%', '50%', '75%', 'max', 'Most Frequently Value','% of MF Values']
    return meta_df

def bad_flag(row):
    if row['std'] == 0:
        return True
#     elif (row['25%'] == row['50%']) and (row['50%'] == row['75%']):
#         return True
    elif row['% of Total Values'] > .15:
        return True
    elif row['% of MF Values'] > .80:
        return True
    elif pd.isnull(row['% of MF Values']):
        return True
    else:
        return False

meta_df = generate_meta_signals(df)
    
meta_df['is Trash var'] = meta_df.apply(lambda r: bad_flag(r), axis=1)
meta_df.head(35)

	Index	Missing Values	% of Total Values	dtypes	count	mean	std	min	25%	50%	75%	max	Most Frequently Value	% of MF Values	is Trash var
0	Age	0	0.0	int64	1470.0	36.923810	9.135373	18.0	30.00	36.0	43.00	60.0	35.0	0.053061	False
1	Attrition	0	0.0	object	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	No	0.838776	True
2	BusinessTravel	0	0.0	object	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Travel_Rarely	0.709524	False
3	DailyRate	0	0.0	int64	1470.0	802.485714	403.509100	102.0	465.00	802.0	1157.00	1499.0	691.0	0.004082	False
4	Department	0	0.0	object	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Research & Development	0.653741	False
5	DistanceFromHome	0	0.0	int64	1470.0	9.192517	8.106864	1.0	2.00	7.0	14.00	29.0	2.0	0.143537	False
6	Education	0	0.0	int64	1470.0	2.912925	1.024165	1.0	2.00	3.0	4.00	5.0	3.0	0.389116	False
7	EducationField	0	0.0	object	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Life Sciences	0.412245	False
8	EmployeeCount	0	0.0	int64	1470.0	1.000000	0.000000	1.0	1.00	1.0	1.00	1.0	1.0	1.0	True
9	EmployeeNumber	0	0.0	int64	1470.0	1024.865306	602.024335	1.0	491.25	1020.5	1555.75	2068.0	2048.0	0.00068	False
10	EnvironmentSatisfaction	0	0.0	int64	1470.0	2.721769	1.093082	1.0	2.00	3.0	4.00	4.0	3.0	0.308163	False
11	Gender	0	0.0	object	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Male	0.6	False
12	HourlyRate	0	0.0	int64	1470.0	65.891156	20.329428	30.0	48.00	66.0	83.75	100.0	66.0	0.019728	False
13	JobInvolvement	0	0.0	int64	1470.0	2.729932	0.711561	1.0	2.00	3.0	3.00	4.0	3.0	0.590476	False
14	JobLevel	0	0.0	int64	1470.0	2.063946	1.106940	1.0	1.00	2.0	3.00	5.0	1.0	0.369388	False
15	JobRole	0	0.0	object	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Sales Executive	0.221769	False
16	JobSatisfaction	0	0.0	int64	1470.0	2.728571	1.102846	1.0	2.00	3.0	4.00	4.0	4.0	0.312245	False
17	MaritalStatus	0	0.0	object	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Married	0.457823	False
18	MonthlyIncome	0	0.0	int64	1470.0	6502.931293	4707.956783	1009.0	2911.00	4919.0	8379.00	19999.0	2342.0	0.002721	False
19	MonthlyRate	0	0.0	int64	1470.0	14313.103401	7117.786044	2094.0	8047.00	14235.5	20461.50	26999.0	4223.0	0.002041	False
20	NumCompaniesWorked	0	0.0	int64	1470.0	2.693197	2.498009	0.0	1.00	2.0	4.00	9.0	1.0	0.354422	False
21	Over18	0	0.0	object	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Y	1.0	True
22	OverTime	0	0.0	object	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	No	0.717007	False
23	PercentSalaryHike	0	0.0	int64	1470.0	15.209524	3.659938	11.0	12.00	14.0	18.00	25.0	11.0	0.142857	False
24	PerformanceRating	0	0.0	int64	1470.0	3.153741	0.360824	3.0	3.00	3.0	3.00	4.0	3.0	0.846259	True
25	RelationshipSatisfaction	0	0.0	int64	1470.0	2.712245	1.081209	1.0	2.00	3.0	4.00	4.0	3.0	0.312245	False
26	StandardHours	0	0.0	int64	1470.0	80.000000	0.000000	80.0	80.00	80.0	80.00	80.0	80.0	1.0	True
27	StockOptionLevel	0	0.0	int64	1470.0	0.793878	0.852077	0.0	0.00	1.0	1.00	3.0	0.0	0.429252	False
28	TotalWorkingYears	0	0.0	int64	1470.0	11.279592	7.780782	0.0	6.00	10.0	15.00	40.0	10.0	0.137415	False
29	TrainingTimesLastYear	0	0.0	int64	1470.0	2.799320	1.289271	0.0	2.00	3.0	3.00	6.0	2.0	0.372109	False
30	WorkLifeBalance	0	0.0	int64	1470.0	2.761224	0.706476	1.0	2.00	3.0	3.00	4.0	3.0	0.607483	False
31	YearsAtCompany	0	0.0	int64	1470.0	7.008163	6.126525	0.0	3.00	5.0	9.00	40.0	5.0	0.133333	False
32	YearsInCurrentRole	0	0.0	int64	1470.0	4.229252	3.623137	0.0	2.00	3.0	7.00	18.0	2.0	0.253061	False
33	YearsSinceLastPromotion	0	0.0	int64	1470.0	2.187755	3.222430	0.0	0.00	1.0	3.00	15.0	0.0	0.395238	False
34	YearsWithCurrManager	0	0.0	int64	1470.0	4.123129	3.568136	0.0	2.00	3.0	7.00	17.0	2.0	0.234014	False

From this table we see that:

• there is no missing values in dataset
• dataset has several trash variables. Let's drop ['EmployeeCount', 'Over18', 'StandardHours']
• our target variable is imbalanced (84% of employees haven't shifted from the company)

| Data Validation

• Drop trash variables
• Encode target variable to numerical

df.drop(['EmployeeCount', 'Over18', 'StandardHours'], axis=1, inplace=True)

from sklearn.preprocessing import LabelEncoder
map_list = []
le_name_mapping = dict()
le = LabelEncoder()
df["Attrition"] = le.fit_transform(df["Attrition"])
le_name_mapping["Attrition"] = dict(zip(le.classes_, le.transform(le.classes_)))

| EDA

• target variable
• correlation matrix with 'attrition'
• correlation matrix
• checking if experience (TotalWorkingYears) is a factor that affects employee from resigning
• checking if age is a factor that affects employees from resigning
• checking if gender is a factor that affects employees from resigning
• checking if travel
• checking if overtime
• checking if monthly income

import plotly.express as px

Target variable

fig = px.pie(df, "Attrition", title="Attrition distribution", hole=.3)
fig.show()

our target variable is imbalanced (84% of employees haven't shifted from the company)

Correlation with target variable

attr_corr = df[(df.select_dtypes(include=np.number).columns.difference(['Attrition']))].corrwith(df["Attrition"]).sort_values(ascending=False)
df_attr_corr = pd.DataFrame(attr_corr)

fig = px.imshow(df_attr_corr, color_continuous_scale='Viridis')
fig.update_layout(
    height=800
)
fig.show()

Tbh there is no significant correlation between any feature with 'Attrition'

Correlation matrix

df_corr = df[(df.select_dtypes(include=np.number).columns.difference(['Attrition']))].corr()
text = df_corr.values.tolist()
fig = px.imshow(df_corr, color_continuous_scale='Viridis', aspect="auto")
fig.update_xaxes(side="top")
fig.show()

We can notice a high correlation between

• 'JobLevel' and ['MonthlyIncome', 'TotalWorkingYears'] - Positive
• 'TotalWorkingYears' and ['Age', 'JobLevel', 'MonthlyIncome'] - Positive
• 'YearsAtCompany' and ['YearsInCurrentRole', 'YearsWithCurrentManager'] - Positive
• 'YearsInCurrentRole' and 'YearsWithCurrentManager' - Positive

Experience

fig = px.histogram(df, x="TotalWorkingYears", color="Attrition")
fig.show()

df["TotalWorkingYears"].corr(df["Attrition"])

-0.17106324613622667

Looking at the graph above we can tell that experience (TotalWorkingYears) has a very weak correlation with employee attrition so we can say that experience is not a major factor on employee attrition

Age

fig = px.histogram(df, x="Age", color="Attrition")
fig.show()

df["Age"].corr(df["Attrition"])

-0.1592050068657797

Age is same as experience (TotalWorkingYears) as they both doesn't have strong correlations towards to employees attrition

Gender

fig = px.histogram(df, x="Gender", color="Attrition", barmode="group")
fig.show()

From the graphs above, all of them has a very weak to no correlations towards employees attrition

Travel

fig = px.histogram(df, x='BusinessTravel', color='Attrition', barmode="group")
fig.show()

Overtime

fig = px.histogram(df, x='OverTime', color='Attrition', barmode="group")
fig.show()

pd.crosstab(df['OverTime'], df['Gender'])

Gender	Female	Male
OverTime
No	408	646
Yes	180	236

Montly Income

fig = px.histogram(df, x="MonthlyIncome", color="Attrition")
fig.show()

Save data for sender

df[df.columns.difference(["Attrition"])].head(2).to_csv("../data/request.csv", index=[0])

| Data Processing for Training

from sklearn.model_selection import train_test_split
from sklearn.feature_extraction import DictVectorizer

from sklearn.metrics import roc_auc_score
from sklearn.preprocessing import MinMaxScaler

Feature Engineering

Label Encoder

# categorical_columns = [col for col in df.columns if df[col].dtype == 'object']
# print(categorical_columns)
# for cat_col in categorical_columns:
#     le = LabelEncoder()
#     df[cat_col] = le.fit_transform(df[cat_col])
#     le_name_mapping[cat_col] = dict(zip(le.classes_, le.transform(le.classes_)))

# le_name_mapping

Train-test Split

df_train, df_test = train_test_split(df, test_size=.2, random_state=1)

y_train, y_test = df_train['Attrition'], df_test['Attrition']
for df_ in [df_train, df_test]:
    del df_['Attrition'] 
y = y_train.append(y_test, ignore_index = True)

One-hot encoding

train_dict = df_train.to_dict(orient='records')
test_dict = df_test.to_dict(orient='records')

# sparse - is a matrix that is comprised of mostly zero values.
dv = DictVectorizer(sparse=False)
dv.fit(train_dict)

DictVectorizer(sparse=False)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

X_train = dv.transform(train_dict)
X_test = dv.transform(test_dict)
X = np.concatenate((X_train, X_test))

dv.get_feature_names()

C:\Users\User\Anaconda\lib\site-packages\sklearn\utils\deprecation.py:87: FutureWarning:

Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.

['Age',
 'BusinessTravel=Non-Travel',
 'BusinessTravel=Travel_Frequently',
 'BusinessTravel=Travel_Rarely',
 'DailyRate',
 'Department=Human Resources',
 'Department=Research & Development',
 'Department=Sales',
 'DistanceFromHome',
 'Education',
 'EducationField=Human Resources',
 'EducationField=Life Sciences',
 'EducationField=Marketing',
 'EducationField=Medical',
 'EducationField=Other',
 'EducationField=Technical Degree',
 'EmployeeNumber',
 'EnvironmentSatisfaction',
 'Gender=Female',
 'Gender=Male',
 'HourlyRate',
 'JobInvolvement',
 'JobLevel',
 'JobRole=Healthcare Representative',
 'JobRole=Human Resources',
 'JobRole=Laboratory Technician',
 'JobRole=Manager',
 'JobRole=Manufacturing Director',
 'JobRole=Research Director',
 'JobRole=Research Scientist',
 'JobRole=Sales Executive',
 'JobRole=Sales Representative',
 'JobSatisfaction',
 'MaritalStatus=Divorced',
 'MaritalStatus=Married',
 'MaritalStatus=Single',
 'MonthlyIncome',
 'MonthlyRate',
 'NumCompaniesWorked',
 'OverTime=No',
 'OverTime=Yes',
 'PercentSalaryHike',
 'PerformanceRating',
 'RelationshipSatisfaction',
 'StockOptionLevel',
 'TotalWorkingYears',
 'TrainingTimesLastYear',
 'WorkLifeBalance',
 'YearsAtCompany',
 'YearsInCurrentRole',
 'YearsSinceLastPromotion',
 'YearsWithCurrManager']

Feature Importance

numerical_vars = list(df[df.columns.difference(["Attrition"])].select_dtypes(include=np.number).columns)

aucs = dict()
for var in numerical_vars:
    auc_ = roc_auc_score(y_train, df_train[var])
    if auc_ < 0.5:
        auc_ = roc_auc_score(y_train, -df_train[var])
    aucs[var] = auc_    

sorted(aucs.items(), key=lambda x: x[1])

[('MonthlyRate', 0.501490505034657),
 ('Education', 0.5064663263533618),
 ('HourlyRate', 0.5100384953743913),
 ('PerformanceRating', 0.510694093453545),
 ('EmployeeNumber', 0.5113496915326987),
 ('RelationshipSatisfaction', 0.514397942430644),
 ('NumCompaniesWorked', 0.5167317595243832),
 ('TrainingTimesLastYear', 0.5218028386836487),
 ('PercentSalaryHike', 0.5256747897323254),
 ('YearsSinceLastPromotion', 0.5261370704291646),
 ('DailyRate', 0.5464466023769633),
 ('JobSatisfaction', 0.5560480323652521),
 ('WorkLifeBalance', 0.56864448092882),
 ('EnvironmentSatisfaction', 0.5697231358881112),
 ('JobInvolvement', 0.5741946509920823),
 ('DistanceFromHome', 0.5742198663028191),
 ('StockOptionLevel', 0.6228742092198383),
 ('Age', 0.6255666440662772),
 ('YearsWithCurrManager', 0.6292396743302534),
 ('JobLevel', 0.6324560272997765),
 ('YearsInCurrentRole', 0.6384432627491413),
 ('TotalWorkingYears', 0.6402727736281469),
 ('YearsAtCompany', 0.6428167183113587),
 ('MonthlyIncome', 0.6477112902954674)]

Feature Selection

Feature Scaling

min_max = MinMaxScaler()
min_max.fit(X_train)
X_train_scaled = min_max.transform(X_train)
X_test_scaled = min_max.transform(X_test)
X_scaled = np.concatenate((X_train_scaled, X_test_scaled))

| Modelling

from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
# from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import export_graphviz
from dtreeviz.trees import *
import graphviz
from sklearn.ensemble import RandomForestClassifier

from catboost import CatBoostClassifier
from xgboost import XGBClassifier

from sklearn.model_selection import KFold
from sklearn.metrics import f1_score, precision_score, recall_score, precision_recall_curve

log_clf = LogisticRegression()
svc_clf = SVC()
# knn_clf = KNeighborsClassifier()
dt_clf = DecisionTreeClassifier()
rf_clf = RandomForestClassifier()
cat_clf = CatBoostClassifier()
xgb_clf = XGBClassifier()

kfold = KFold(n_splits=5, shuffle=True, random_state=1)
scores = dict()

for clf in [log_clf, svc_clf, dt_clf, rf_clf, cat_clf, xgb_clf]:
    clf_list = []
    for train_idxs, test_idxs in kfold.split(X):
        X_train, X_test = X[train_idxs], X[test_idxs]
        y_train, y_test = y[train_idxs], y[test_idxs]

        clf.fit(X_train, y_train)
        y_pred = clf.predict(X_test)
        clf_list.append(roc_auc_score(y_test, y_pred))
    scores[clf.__class__.__name__] = clf_list

C:\Users\User\Anaconda\lib\site-packages\sklearn\linear_model\_logistic.py:444: ConvergenceWarning:

lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression

C:\Users\User\Anaconda\lib\site-packages\sklearn\linear_model\_logistic.py:444: ConvergenceWarning:

lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression

C:\Users\User\Anaconda\lib\site-packages\sklearn\linear_model\_logistic.py:444: ConvergenceWarning:

lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression

C:\Users\User\Anaconda\lib\site-packages\sklearn\linear_model\_logistic.py:444: ConvergenceWarning:

lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression

C:\Users\User\Anaconda\lib\site-packages\sklearn\linear_model\_logistic.py:444: ConvergenceWarning:

lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression

Learning rate set to 0.011041
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998:	learn: 0.1098101	total: 4.87s	remaining: 4.88ms
999:	learn: 0.1096377	total: 4.88s	remaining: 0us

for key in scores.keys():
    print(f'{key}' + ' ' + f'{np.mean(scores[key]):.3f} +- {np.std(scores[key]):.3f}')

LogisticRegression 0.514 +- 0.011
SVC 0.500 +- 0.000
DecisionTreeClassifier 0.611 +- 0.028
RandomForestClassifier 0.592 +- 0.016
CatBoostClassifier 0.614 +- 0.018
XGBClassifier 0.664 +- 0.031

kfold = KFold(n_splits=5, shuffle=True, random_state=1)
scores = dict()

for clf in [log_clf, svc_clf, dt_clf, rf_clf, cat_clf, xgb_clf]:
    clf_list = []
    for train_idxs, test_idxs in kfold.split(X_scaled):
        X_train_scaled, X_test_scaled = X_scaled[train_idxs], X_scaled[test_idxs]
        y_train_scaled, y_test_scaled = y[train_idxs], y[test_idxs]

        clf.fit(X_train_scaled, y_train_scaled)
        y_pred = clf.predict(X_test_scaled)
        clf_list.append(roc_auc_score(y_test_scaled, y_pred))
    scores[clf.__class__.__name__] = clf_list

Learning rate set to 0.011041
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1:	learn: 0.6752674	total: 13ms	remaining: 6.47s
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for key in scores.keys():
    print(f'{key}' + ' ' + f'{np.mean(scores[key]):.3f} +- {np.std(scores[key]):.3f}')

LogisticRegression 0.680 +- 0.016
SVC 0.597 +- 0.026
DecisionTreeClassifier 0.608 +- 0.014
RandomForestClassifier 0.590 +- 0.026
CatBoostClassifier 0.614 +- 0.018
XGBClassifier 0.664 +- 0.031

This is reason why Feature Scaling is used. by scaling down the features we are able to achieve better accuracy

Performance tuning (hyperparameters)

from sklearn.model_selection import RandomizedSearchCV

LR

solvers = ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga']

penalty = ['none', 'l1', 'l2', 'elasticnet']

c_values = [100, 10, 1.0, 0.1, 0.01]

lr_grid = dict(solver=solvers, penalty=penalty, C=c_values)
print(lr_grid)

{'solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'], 'penalty': ['none', 'l1', 'l2', 'elasticnet'], 'C': [100, 10, 1.0, 0.1, 0.01]}

lr_random = RandomizedSearchCV(log_clf, param_distributions=lr_grid, scoring='roc_auc', cv=5, n_iter=20,
                               random_state=1, n_jobs=-1, verbose=1)
lr_random.fit(X_train_scaled, y_train_scaled)

Fitting 5 folds for each of 20 candidates, totalling 100 fits

C:\Users\User\Anaconda\lib\site-packages\sklearn\model_selection\_validation.py:378: FitFailedWarning:


35 fits failed out of a total of 100.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.

Below are more details about the failures:
--------------------------------------------------------------------------------
5 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\User\Anaconda\lib\site-packages\sklearn\model_selection\_validation.py", line 686, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\User\Anaconda\lib\site-packages\sklearn\linear_model\_logistic.py", line 1091, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "C:\Users\User\Anaconda\lib\site-packages\sklearn\linear_model\_logistic.py", line 71, in _check_solver
    raise ValueError(
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

--------------------------------------------------------------------------------
10 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\User\Anaconda\lib\site-packages\sklearn\model_selection\_validation.py", line 686, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\User\Anaconda\lib\site-packages\sklearn\linear_model\_logistic.py", line 1091, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "C:\Users\User\Anaconda\lib\site-packages\sklearn\linear_model\_logistic.py", line 61, in _check_solver
    raise ValueError(
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
5 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\User\Anaconda\lib\site-packages\sklearn\model_selection\_validation.py", line 686, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\User\Anaconda\lib\site-packages\sklearn\linear_model\_logistic.py", line 1091, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "C:\Users\User\Anaconda\lib\site-packages\sklearn\linear_model\_logistic.py", line 78, in _check_solver
    raise ValueError("penalty='none' is not supported for the liblinear solver")
ValueError: penalty='none' is not supported for the liblinear solver

--------------------------------------------------------------------------------
5 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\User\Anaconda\lib\site-packages\sklearn\model_selection\_validation.py", line 686, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\User\Anaconda\lib\site-packages\sklearn\linear_model\_logistic.py", line 1091, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "C:\Users\User\Anaconda\lib\site-packages\sklearn\linear_model\_logistic.py", line 61, in _check_solver
    raise ValueError(
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
5 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\User\Anaconda\lib\site-packages\sklearn\model_selection\_validation.py", line 686, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\User\Anaconda\lib\site-packages\sklearn\linear_model\_logistic.py", line 1101, in fit
    raise ValueError(
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

--------------------------------------------------------------------------------
5 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\User\Anaconda\lib\site-packages\sklearn\model_selection\_validation.py", line 686, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\User\Anaconda\lib\site-packages\sklearn\linear_model\_logistic.py", line 1091, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "C:\Users\User\Anaconda\lib\site-packages\sklearn\linear_model\_logistic.py", line 61, in _check_solver
    raise ValueError(
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.


C:\Users\User\Anaconda\lib\site-packages\sklearn\model_selection\_search.py:953: UserWarning:

One or more of the test scores are non-finite: [0.82221051 0.82253575 0.82337105 0.82228667 0.79034627        nan
        nan        nan 0.74541396        nan 0.79809721        nan
        nan 0.82264515 0.82332425 0.82329575 0.8233728  0.82245982
        nan 0.8225874 ]

C:\Users\User\Anaconda\lib\site-packages\sklearn\linear_model\_logistic.py:444: ConvergenceWarning:

lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression

RandomizedSearchCV(cv=5, estimator=LogisticRegression(), n_iter=20, n_jobs=-1,
                   param_distributions={'C': [100, 10, 1.0, 0.1, 0.01],
                                        'penalty': ['none', 'l1', 'l2',
                                                    'elasticnet'],
                                        'solver': ['newton-cg', 'lbfgs',
                                                   'liblinear', 'sag',
                                                   'saga']},
                   random_state=1, scoring='roc_auc', verbose=1)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

y_pred_lr = lr_random.predict_proba(X_test_scaled)[:, 1]

print(lr_random.best_score_)
print(lr_random.best_params_)

0.8233727974313843
{'solver': 'lbfgs', 'penalty': 'l2', 'C': 10}

KNN

# n_neighbors = [int(x) for x in np.arange(1,22,1)]

# metric = ['eucledian', 'manhattan', 'minkowski']

# weights = ['uniform', 'distance']

# knn_grid = dict(n_neighbors=n_neighbors, weights=weights, metric=metric)
# print(knn_grid)

# knn_random = RandomizedSearchCV(knn_clf, param_distributions=knn_grid, scoring='roc_auc', cv=5, n_iter=20,
#                                random_state=1, n_jobs=-1, verbose=1)
# knn_random.fit(X_train_scaled, y_train_scaled)

# y_pred_knn = knn_random.predict_proba(X_test_scaled)[:, 1]

# print(knn_random.best_score_)
# print(knn_random.best_params_)

SVC

kernel = ['poly', 'rbf', 'sigmoid']
C = [50, 10, 1.0, 0.1, 0.01]
gamma = ['scale']

svc_grid = dict(kernel=kernel,C=C,gamma=gamma)
print(svc_grid)

{'kernel': ['poly', 'rbf', 'sigmoid'], 'C': [50, 10, 1.0, 0.1, 0.01], 'gamma': ['scale']}

svc_random = RandomizedSearchCV(svc_clf, param_distributions=svc_grid, scoring='roc_auc', cv=5, n_iter=20,
                               random_state=1, n_jobs=-1, verbose=1)
svc_random.fit(X_train_scaled, y_train_scaled)

Fitting 5 folds for each of 15 candidates, totalling 75 fits

C:\Users\User\Anaconda\lib\site-packages\sklearn\model_selection\_search.py:306: UserWarning:

The total space of parameters 15 is smaller than n_iter=20. Running 15 iterations. For exhaustive searches, use GridSearchCV.

RandomizedSearchCV(cv=5, estimator=SVC(), n_iter=20, n_jobs=-1,
                   param_distributions={'C': [50, 10, 1.0, 0.1, 0.01],
                                        'gamma': ['scale'],
                                        'kernel': ['poly', 'rbf', 'sigmoid']},
                   random_state=1, scoring='roc_auc', verbose=1)

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print(svc_random.best_score_)
print(svc_random.best_params_)

0.8043840222303889
{'kernel': 'rbf', 'gamma': 'scale', 'C': 1.0}

DT

import re
def draw_tree(t, df, size=10, ratio=0.6, precision=2, **kwargs):
    s=export_graphviz(t, out_file=None, feature_names=df.columns, filled=True, rounded=True,
                      special_characters=True, rotate=False, precision=precision, **kwargs)
    return graphviz.Source(re.sub('Tree {', f'Tree {{ size={size}; ratio={ratio}', s))

dt = DecisionTreeClassifier(max_leaf_nodes=4)
dt.fit(X_train_scaled, y_train_scaled)

DecisionTreeClassifier(max_leaf_nodes=4)

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draw_tree(dt, pd.DataFrame(X_train_scaled, columns=dv.get_feature_names()),
          size=10, leaves_parallel=True, precision=2)

C:\Users\User\Anaconda\lib\site-packages\sklearn\utils\deprecation.py:87: FutureWarning:

Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.

samp_idx = np.random.permutation(len(y_train_scaled))[:500]
dtreeviz(dt, pd.DataFrame(X_train_scaled, columns=dv.get_feature_names()).iloc[samp_idx, ], y_train_scaled.iloc[samp_idx], pd.DataFrame(X_train_scaled, columns=dv.get_feature_names()).columns, "stroke",
         fontname='DejaVu Sans', scale=1.6, label_fontsize=10,
         orientation='LR')

RF

#number of trees in forest
n_estimators = [int(x) for x in np.linspace(100,1200,12)]
#max depth of tree
max_depth = [int(x) for x in np.linspace(5,30,6)]
#quality of split
criterion = ['gini','entropy']
#min no. of samples to consider for splitting a internal node
min_samples_split = [2,5,7,10]
#min number of node can be as leaf node
min_samples_leaf = [2,5,8]
# The number of features to consider when looking for the best split:
max_features = ["auto","sqrt"]

random_grid = dict(n_estimators=n_estimators, max_depth=max_depth, criterion=criterion,
                  min_samples_split=min_samples_split,min_samples_leaf=min_samples_leaf,
                   max_features=max_features)
print(random_grid)

{'n_estimators': [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200], 'max_depth': [5, 10, 15, 20, 25, 30], 'criterion': ['gini', 'entropy'], 'min_samples_split': [2, 5, 7, 10], 'min_samples_leaf': [2, 5, 8], 'max_features': ['auto', 'sqrt']}

rf_random = RandomizedSearchCV(rf_clf, param_distributions=random_grid, scoring='roc_auc', cv=5, n_iter=20,
                               random_state=1, n_jobs=-1, verbose=1)

rf_random.fit(X_train_scaled, y_train_scaled)

Fitting 5 folds for each of 20 candidates, totalling 100 fits

RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=20,
                   n_jobs=-1,
                   param_distributions={'criterion': ['gini', 'entropy'],
                                        'max_depth': [5, 10, 15, 20, 25, 30],
                                        'max_features': ['auto', 'sqrt'],
                                        'min_samples_leaf': [2, 5, 8],
                                        'min_samples_split': [2, 5, 7, 10],
                                        'n_estimators': [100, 200, 300, 400,
                                                         500, 600, 700, 800,
                                                         900, 1000, 1100,
                                                         1200]},
                   random_state=1, scoring='roc_auc', verbose=1)

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y_pred_rf = rf_random.predict_proba(X_test_scaled)[:, 1]

print(rf_random.best_score_)
print(rf_random.best_params_)

0.7979742235804596
{'n_estimators': 500, 'min_samples_split': 10, 'min_samples_leaf': 5, 'max_features': 'sqrt', 'max_depth': 20, 'criterion': 'gini'}

CatB

n_estimators = [int(x) for x in np.linspace(100, 1000, 10)]

max_depth = [int(x) for x in np.linspace(6, 30, 5)]

learning_rate = [x for x in np.arange(0.001, 0.4, 0.001)]

l2_leaf_reg = [x for x in np.arange(0, 4, 0.1)]

min_data_in_leaf = np.random.randint(20, 200, 5)

cat_grid = dict(n_estimators=n_estimators, max_depth=max_depth, learning_rate=learning_rate,
                l2_leaf_reg=l2_leaf_reg, min_data_in_leaf=min_data_in_leaf)

# cat_random = RandomizedSearchCV(cat_clf, param_distributions=cat_grid, cv=5, random_state=1,
#                                 n_iter=20, scoring="roc_auc", n_jobs=-1, verbose=1)
# cat_random.fit(X_train_scaled,y_train_scaled)

# y_pred_cat = cat_random.predict_proba(X_test_scaled)[:, 1]

# print("score: ", cat_random.best_score_)
# print("best_params: \n", cat_random.best_params_)

XGB

n_estimators = [int(x) for x in np.linspace(100, 1000, 10)]

max_depth = [int(x) for x in np.linspace(6, 30, 5)]

learning_rate = [x for x in np.arange(0.001, 0.4, 0.001)]

# how many observations we need to have in a leaf node
min_child_weight = list(range(1, 10))

xg_grid = dict(n_estimators=n_estimators, max_depth=max_depth, learning_rate=learning_rate,
               min_child_weight=min_child_weight)
print(xg_grid)

{'n_estimators': [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000], 'max_depth': [6, 12, 18, 24, 30], 'learning_rate': [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009000000000000001, 0.010000000000000002, 0.011, 0.012, 0.013000000000000001, 0.014000000000000002, 0.015, 0.016, 0.017, 0.018000000000000002, 0.019000000000000003, 0.02, 0.021, 0.022000000000000002, 0.023, 0.024, 0.025, 0.026000000000000002, 0.027000000000000003, 0.028, 0.029, 0.030000000000000002, 0.031, 0.032, 0.033, 0.034, 0.035, 0.036000000000000004, 0.037000000000000005, 0.038, 0.039, 0.04, 0.041, 0.042, 0.043000000000000003, 0.044000000000000004, 0.045, 0.046, 0.047, 0.048, 0.049, 0.05, 0.051000000000000004, 0.052000000000000005, 0.053000000000000005, 0.054, 0.055, 0.056, 0.057, 0.058, 0.059000000000000004, 0.060000000000000005, 0.061, 0.062, 0.063, 0.064, 0.065, 0.066, 0.067, 0.068, 0.069, 0.07, 0.07100000000000001, 0.07200000000000001, 0.07300000000000001, 0.074, 0.075, 0.076, 0.077, 0.078, 0.079, 0.08, 0.081, 0.082, 0.083, 0.084, 0.085, 0.08600000000000001, 0.08700000000000001, 0.08800000000000001, 0.089, 0.09, 0.091, 0.092, 0.093, 0.094, 0.095, 0.096, 0.097, 0.098, 0.099, 0.1, 0.101, 0.10200000000000001, 0.10300000000000001, 0.10400000000000001, 0.10500000000000001, 0.106, 0.107, 0.108, 0.109, 0.11, 0.111, 0.112, 0.113, 0.114, 0.115, 0.116, 0.117, 0.11800000000000001, 0.11900000000000001, 0.12000000000000001, 0.121, 0.122, 0.123, 0.124, 0.125, 0.126, 0.127, 0.128, 0.129, 0.13, 0.131, 0.132, 0.133, 0.134, 0.135, 0.136, 0.137, 0.138, 0.139, 0.14, 0.14100000000000001, 0.14200000000000002, 0.14300000000000002, 0.14400000000000002, 0.14500000000000002, 0.146, 0.147, 0.148, 0.149, 0.15, 0.151, 0.152, 0.153, 0.154, 0.155, 0.156, 0.157, 0.158, 0.159, 0.16, 0.161, 0.162, 0.163, 0.164, 0.165, 0.166, 0.167, 0.168, 0.169, 0.17, 0.171, 0.17200000000000001, 0.17300000000000001, 0.17400000000000002, 0.17500000000000002, 0.17600000000000002, 0.177, 0.178, 0.179, 0.18, 0.181, 0.182, 0.183, 0.184, 0.185, 0.186, 0.187, 0.188, 0.189, 0.19, 0.191, 0.192, 0.193, 0.194, 0.195, 0.196, 0.197, 0.198, 0.199, 0.2, 0.201, 0.202, 0.203, 0.20400000000000001, 0.20500000000000002, 0.20600000000000002, 0.20700000000000002, 0.20800000000000002, 0.20900000000000002, 0.21, 0.211, 0.212, 0.213, 0.214, 0.215, 0.216, 0.217, 0.218, 0.219, 0.22, 0.221, 0.222, 0.223, 0.224, 0.225, 0.226, 0.227, 0.228, 0.229, 0.23, 0.231, 0.232, 0.233, 0.234, 0.23500000000000001, 0.23600000000000002, 0.23700000000000002, 0.23800000000000002, 0.23900000000000002, 0.24000000000000002, 0.241, 0.242, 0.243, 0.244, 0.245, 0.246, 0.247, 0.248, 0.249, 0.25, 0.251, 0.252, 0.253, 0.254, 0.255, 0.256, 0.257, 0.258, 0.259, 0.26, 0.261, 0.262, 0.263, 0.264, 0.265, 0.266, 0.267, 0.268, 0.269, 0.27, 0.271, 0.272, 0.273, 0.274, 0.275, 0.276, 0.277, 0.278, 0.279, 0.28, 0.281, 0.28200000000000003, 0.28300000000000003, 0.28400000000000003, 0.28500000000000003, 0.28600000000000003, 0.28700000000000003, 0.28800000000000003, 0.28900000000000003, 0.29, 0.291, 0.292, 0.293, 0.294, 0.295, 0.296, 0.297, 0.298, 0.299, 0.3, 0.301, 0.302, 0.303, 0.304, 0.305, 0.306, 0.307, 0.308, 0.309, 0.31, 0.311, 0.312, 0.313, 0.314, 0.315, 0.316, 0.317, 0.318, 0.319, 0.32, 0.321, 0.322, 0.323, 0.324, 0.325, 0.326, 0.327, 0.328, 0.329, 0.33, 0.331, 0.332, 0.333, 0.334, 0.335, 0.336, 0.337, 0.338, 0.339, 0.34, 0.341, 0.342, 0.343, 0.34400000000000003, 0.34500000000000003, 0.34600000000000003, 0.34700000000000003, 0.34800000000000003, 0.34900000000000003, 0.35000000000000003, 0.35100000000000003, 0.35200000000000004, 0.353, 0.354, 0.355, 0.356, 0.357, 0.358, 0.359, 0.36, 0.361, 0.362, 0.363, 0.364, 0.365, 0.366, 0.367, 0.368, 0.369, 0.37, 0.371, 0.372, 0.373, 0.374, 0.375, 0.376, 0.377, 0.378, 0.379, 0.38, 0.381, 0.382, 0.383, 0.384, 0.385, 0.386, 0.387, 0.388, 0.389, 0.39, 0.391, 0.392, 0.393, 0.394, 0.395, 0.396, 0.397, 0.398, 0.399], 'min_child_weight': [1, 2, 3, 4, 5, 6, 7, 8, 9]}

xgb_random = RandomizedSearchCV(xgb_clf, param_distributions=xg_grid, cv=5, random_state=1,
                                n_iter=20, scoring="roc_auc", n_jobs=-1, verbose=1)
xgb_random.fit(X_train_scaled, y_train_scaled)

Fitting 5 folds for each of 20 candidates, totalling 100 fits

y_pred_xgb = xgb_random.predict_proba(X_test_scaled)[:, 1]

print("score: ",xgb_random.best_score_)
print("best_params: \n",xgb_random.best_params_)

Create a confusion matrix

thresholds = np.linspace(0, 1, 101)

def confusion_matrix_dataframe(y_val, y_pred):
    scores = []
    for t in thresholds:
        actual_positive = (y_val == 1)
        actual_negative = (y_val == 0)

        predict_positive = (y_pred >= t)
        predict_negative = (y_pred < t)

        tp = (predict_positive & actual_positive).sum()
        tn = (predict_negative & actual_negative).sum()

        fp = (predict_positive & actual_negative).sum()
        fn = (predict_negative & actual_positive).sum()

        tpr = tp/(tp + fn)
        fpr = fp/(fp + tn)
        
        precision = tp / (tp + fp)
        recall = tp / (tp + fn)
        
        f1 = 2 * precision * recall / (precision+recall)
        
        gmean = np.sqrt(tpr * (1-fpr))
        
        auc = (1+tpr-fpr)/2
        
        scores.append((t, tp, fp, fn, tn, tpr, fpr, precision, recall, f1, gmean, auc))

    columns = ['threshold', 'tp', 'fp', 'fn', 'tn', 'tpr', 'fpr', 'precision', 'recall', 'f1', 'gmean', 'auc']
    df_scores = pd.DataFrame(scores, columns=columns)
    
    return df_scores

df_scores = confusion_matrix_dataframe(y_test_scaled, y_pred_lr)
df_scores.head()

Find threshold

import plotly.graph_objects as go

# locate the index of the largest g-mean
ix = np.argmax(df_scores.gmean)
print('Best Threshold=%f, G-Mean=%.3f' % (df_scores.threshold[ix], df_scores.gmean[ix]))

df_scores[df_scores.precision==df_scores.recall]

fig = go.Figure()
fig.add_trace(go.Scatter(x=df_scores.threshold, y=df_scores.precision,
                    mode='lines',
                    name='precision'))
fig.add_trace(go.Scatter(x=df_scores.threshold, y=df_scores.recall,
                    mode='lines',
                    name='recall'))
fig.add_vline(0.26, line_dash="dot")
fig.show()

fig = go.Figure()
fig.add_trace(go.Scatter(x=df_scores.threshold, y=df_scores.f1,
                    mode='lines',
                    name='lines'))
fig.add_vline(0.25, line_dash="dot")
fig.show()

fig = go.Figure()
fig.add_trace(go.Scatter(x=df_scores.fpr, y=df_scores.tpr,
                    mode='lines'))
fig.update_layout(
    title="ROC Curve",
    xaxis_title="False Positive Rate",
    yaxis_title="True Positive Rate",
)
fig.add_vline(0.15, line_dash="dot")
fig.show()

| Make html

!jupyter nbconvert --to html "EDA, Modelling + Tuning.ipynb"